Abstract
The one of many significant particularities of satellite imagery is large size of images within orders of magnitude exceeds capability of modern GPGPU to train neural networks on its full size. On the other hand satellite imagery tends to be limitedly available. Moreover, the objects of interest tends to constitute a small fraction of whole dataset. This leads to the demand of sample extraction and augmentation method specialized on satellite imagery. Yet this area is immensely underrated so almost all widely used method limited to grid-based sample extraction and augmentation via combinations of 90-degrees rotations and mirroring on vertical or horizontal axes. This paper proposes the domain-agnostic method of sample extraction and augmentation. Adoption of this method to specific subject area is based on domain-specific way to generate significance field of image. In contrast to trivial greedy solutions and more advanced stochastic optimization methods the design of proposed method is focused on maximizing per-step progress. This makes its performance reasonably good even without low-level optimizations without significant quality loss. It can be easily implemented using widely known and open source software libraries.
Highlights
The design and implementation of data preparation pipeline is essential part of building any machine learning model
The most important prospective research is applying developed method in a streaming scenario for generating training samples based on significance field built based on feedback from current accuracy metrics of model on different parts of source images. It known that artificial neural network can show state-of-the-art results in image processing task
This indicates that mentioned issues are immensely underrated
Summary
The design and implementation of data preparation pipeline is essential part of building any machine learning model. Gaining the best results relies not just on deep understanding of particularities and nuances of subject area, involved data sources and models yet on exploiting them ingenuently and efficiently. Processing satellite imagery is one of such distinct areas. It very different from ordinary visual experience and photographic technologies. The satellite images can exceed resolution of 30000x30000 pixels, contain arbitrary number of spectral channels of different spatial resolution. They can contain void areas, that have no data at all. The objects of interest usually quantitatively and arealy unbalanced with its environment within an orders of magnitude
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